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Financial Data Analysis Using Python
Paperback
December 2024
9781501523861
More details
- Publisher
Mercury Learning and Information - Published
19th December - ISBN 9781501523861
- Language English
- Pages 480 pp.
- Size 7" x 9"
$54.99
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December 2024
9781501521843
More details
- Publisher
Mercury Learning and Information - ISBN 9781501521843
- Language English
- Pages 480 pp.
- Size 7" x 9"
$165.00
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December 2024
9781501521881
More details
- Publisher
Mercury Learning and Information - ISBN 9781501521881
- Language English
- Pages 480 pp.
- Size 7" x 9"
$54.99
This book will introduce essential concepts in financial analysis methods and models, covering time-series analysis, graphical analysis, technical and fundamental analysis, asset pricing and portfolio theory, investment and trade strategies, risk assessment and prediction, and financial ML practices. The Python programming language and its ecosystem libraries, such as Pandas, NumPy, SciPy, statsmodels, Matplotlib, Seaborn, Scikit-learn, Prophet, and other data science tools will demonstrate these rooted financial concepts in practice examples. This book will also help you understand the concepts of financial market dynamics, estimate the metrics of financial asset profitability, predict trends, evaluate strategies, optimize portfolios, and manage financial risks. You will also learn data analysis techniques using the Python programming language to understand the basics of data preparation, visualization, and manipulation in the world of financial data.
FEATURES
- Illustrates financial data analysis using Python data science libraries and techniques
- Uses Python visualization tools to justify investment and trading strategies
- Covers asset pricing and portfolio management methods with Python
1: Getting Started with Python for Finance
2: Python Tools for Data Analysis: Primer to Pandas and NumPy
3: Financial Data Manipulation with Python
4: Exploratory Data Analysis for Finance
5: Investment & Trading Strategies
6: Asset Pricing & Portfolio Management
7: Time Series Analysis & Financial Data Forecasting
8: Risk Assessment & Volatility Modelling
9: Machine Learning & Deep Learning in Finance
10: Time Series Analysis & Forecasting with the FB Prophet Library
Appendices:
A: Python Code Examples for Finance
B: Glossary
C: Valuable Resources
Dmytro Zherlitsyn
Dmytro Zherlitsyn, PhD, has dedicated over 20 years to university teaching, business training, financial consulting, scientific research, and data analysis. He has authored over 250 academic publications (e-learning courses, textbooks, scientific papers, and monographs) in Economics, Finance, Data Science, System Analysis, and Software Engineering. His work encompasses the development of predictive models for business and market analysis, including advanced regression, simulation, and machine learning methods for financial sectors, and the cryptocurrency market.
Python programming; Finance market; Investment return; FP and A, Volatility; Data Analysis; Data Visualization; Investment strategy; NumPy; Pandas